Mohammadreza Kamaldar

Mohammadreza Kamaldar, Jesse B. Hoagg

This paper presents an adaptive harmonic steady-state (AHSS) controller, which addresses the problem of rejecting sinusoids with known frequencies that act on a completely unknown multi-input multi-output linear time-invariant system. We analyze the stability and closed-loop performance of AHSS for single-input single-output systems. In this case, we show that AHSS asymptotically rejects disturbances.

Mohammadreza Kamaldar

Deploying neural-network control barrier functions (CBFs) on embedded hardware requires evaluating the barrier value and its Lie derivatives along the system vector fields at every control cycle. The standard mechanism for exact gradient extraction, reverse-mode automatic differentiation, constructs a dynamic computational graph whose memory footprint grows with network depth and whose backward traversal obstructs the worst-case execution time analysis required for safety-critical certification. This paper presents a dual-algebraic compiler that extracts the exact barrier value and its Lie derivatives through forward network evaluation alone. Encoding the system state as the real part of a dual number and a target vector field as the dual part, we prove that every affine and componentwise-activation layer admits a dual extension that propagates the exact directional derivative alongside the activation, and that the composed dual-extended network evaluates the exact Jacobian--vector-field product with zero truncation error. We derive closed-form expressions for the dual-pass floating-point operation count and peak memory footprint, prove that the proposed algorithm eliminates dynamic graph allocation, and extend the framework to the second-order Lie derivatives required by relative-degree-two CBFs using hyper-dual arithmetic. An open-source ahead-of-time compiler translates trained neural CBFs into self-contained C++ headers that assemble the complete safety constraint on an ESP32-S3 microcontroller from a statically allocated buffer, with zero dynamic memory allocation and a sub-millisecond cycle budget that supports kilohertz-rate safety filters.

Mohammadreza Kamaldar

This paper presents a state- and control-dependent moving-horizon estimation (SCD-MHE) algorithm for nonlinear discrete-time systems. Within this framework, a pseudo-linear representation of nonlinear dynamics is leveraged utilizing state- and control-dependent coefficients, where the solution to a moving-horizon estimation problem is iteratively refined. At each discrete time step, a quadratic program is executed over a sliding window of historical measurements. Moreover, system matrices are consecutively updated based upon prior iterates to capture nonlinear regimes. In contrast to the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), nonlinearities and bounds are accommodated within a structured optimization framework, thereby circumventing the reliance on local Jacobian matrices. Furthermore, theoretical analysis is presented to establish the convergence of the iterative sequence, and bounded estimation errors are mathematically guaranteed under uniform observability conditions. Finally, comparative numerical experiments utilizing a quadrotor vertical kinematics system demonstrate that the SCD-MHE achieves superior estimation accuracy relative to the EKF, the UKF, and a fully nonlinear moving-horizon estimator, while reducing per-step computational latency by over an order of magnitude.